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Inferring detailed 3D geometry of the scene is crucial for roboticsapplications, simulation, and 3D content creation. However, such information ishard to obtain, and thus very few datasets support it. In this paper, wepropose an interactive framework for annotating 3D object geometry from bothpoint cloud data and RGB imagery. The key idea behind our approach is toexploit strong priors that humans have about the 3D world in order tointeractively annotate complete 3D shapes. Our framework targets naive userswithout artistic or graphics expertise. We introduce two simple-to-useinteraction modules. First, we make an automatic guess of the 3D shape andallow the user to provide feedback about large errors by drawing scribbles indesired 2D views. Next, we aim to correct minor errors, in which users drag anddrop mesh vertices, assisted by a neural interactive module implemented as aGraph Convolutional Network. Experimentally, we show that only a few userinteractions are needed to produce good quality 3D shapes on popular benchmarkssuch as ShapeNet, Pix3D and ScanNet. We implement our framework as a webservice and conduct a user study, where we show that user annotated data usingour method effectively facilitates real-world learning tasks. Web service:http://www.cs.toronto.edu/~shenti11/scribble3d.

Interactive Annotation of 3D Object Geometry using 2D Scribbles